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NAHR: A Visual Representation of Social Networks asSupport for Art History Research
Houda LamqaddamKU leuven
Leuven, [email protected]
Jan AertsKU Leuven
Leuven, [email protected]
Koenraad BrosensKU Leuven
Leuven, [email protected]
Katrien VerbertKU Leuven
Leuven, [email protected]
ABSTRACTIn the field of art history, the analysis of community dynam-ics can give researchers precious insight on the subjects oftheir studies. Data on genealogy and community bonds canprovide a rich understanding of the functioning of a com-munity. Traditional family trees are not designed to supportextra-familial links and often lack the time-bound aspect ofthese relationships, and timeline-style tools miss the mark onrepresenting the network dimension of such structures. Weintroduce NAHR (Networks in Art History Research), a toolthat visualizes small networks of families connected throughmarriage, god-parenthood and professional relationships, andthat provides insight in the change of these dynamics overtime.
Author KeywordsVisualization; digital humanities; art history; user-centereddesign; network visualization; genealogical data.
ACM Classification KeywordsH.5.m. Information Interfaces and Presentation (e.g. HCI):Miscellaneous
INTRODUCTIONFor art historians, the analysis of personal and professionalrelationships can help understand the dynamics of a commu-nity. Researchers often have to reconstruct artists’ trajectoriesthrough the data they have access to, describing their per-sonal lives (parents’ identities, spouses, children) as well astheir artistic or professional collaborations (pupils, businesspartners, etc.). The access to a comprehensive representationof such data is therefore essential in validating researchers’
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DOI: https://doi.org/10.1145/3240167.3240199
hypotheses, answering existing research questions and trig-gering new ones. Because the described connections mostlyrevolve around family structures, we consider the literaturesurrounding genealogical data representation as a base for ourwork. We find that, although common, the traditional "fam-ily tree" visualization presents numerous shortcomings whenattempting to represent complex familial stories.
Our contribution is a novel approach to the issue of represent-ing changing complex community dynamics through time inthe aim of supporting art history research. While this paperpresents the designed and developed proof of concept, a liveversion is being made available for our collaborators. This willallow them to search the database for any stored person andvisualize the networks of their family and relationship links.By designing the visualization to address the actual needs ofart historians, we also demonstrate how visualizations need tobe tailored for use in the digital humanities field.
RELATED WORKThe representation of genealogical data has been a topic ofinterest in many fields including history, sociology, and vi-sualization. Several tools aim at tackling the difficulties ofrepresenting such complex, hierarchical and time-dependentdata.
A few constraints have been described in the literature. Thefirst is the difficulty of integrating temporal information intree-like structures, and hierarchical genealogical informationin timelines. Scalability is often cited [5] as another limit tovisualizing family trees. The more community members a toolaims at representing, the higher the likelihood of clutter, andthe more readability is impeded by edge-crossing. Moreover,family trees often do not depict full ancestry links, but focus ononly one parent lineage to conserve the tree format. Traditionalfamily tree representations also do not generally enable theportrayal of complex community and family links such asdivorce, remarriages, and out-of-wedlock births.
Finally, the contemporaneity of persons and relationships isanother critical element to understanding community relation-ships. The representation of this aspect is often overlooked.
Figure 1. (a), (b), and (c) represent the ancestry, descendence and ego-centric genealogy representations. (d) was introduced by McGuffin asan alternative method to represent similar data.
This section defines two broad genres of visualizations asdescribed in literature: some based on the family structureand relations between individuals, and others on the temporalaspect of the data. A third category that attempts to give equalimportance to these two aspects is also described.
Family Tree based representationsOne of the simplest representations of genealogical data isthrough trees and tree-like encodings (e.g. radial, treemap)[5]. These graphs represent the individuals as nodes and rela-tionships as links, and are often based on the family structurelayout [8] [5]. Three main use-cases - and therefore representa-tions - have been described as scenarios for tree visualizationsof genealogical data: ancestry, descendance, and ego-centricresearch [9] (see Figure 1). These representations present aclear view of family links and relationships, but they haveimportant setbacks. First, they generally fail in including thetemporal aspect of the data, that is critical in showing contem-poraneity, which in turn is important in assisting the task ofanalyzing community dynamics. An additional constraint isthe limited scalability of this type of graphs, as edge crossinggrows rapidly when the number of individuals representedincreases.
Timeline based representationsOther tools for genealogical data representations were de-signed to center the time aspect of the datasets rather thanthe family structures. One of the first examples of a familytree representation belongs in this group. The "Specimen of aChart of Biography" [11] takes the shape of a timeline, whereindividual actors are represented by horizontal bars, startingin the year they were born and ending on their death year(see Figure 2). Since then, multiple tools with similar struc-tures have been introduced, including in commercial software.Prominent examples are the Genelines interface from ProgenyGenealogy [1] and the TimeLine view of the MacFamilyTree[13], which both base their representations heavily on time,and encode family relationships through annotations.
Combined family tree and timeline based representationsSome works have attempted to bridge the gap between theprevious two categories. Rundall Munroe’s "Movie narrativecharts" [10] and Kim’s TimeNets [6] are, for instance, based ontimeline representations in which individuals’ connections areshown through individual timeline movement. To tackle largerscale data, Reda et al’s community representation tool also
Figure 2. A specimen of Priestley’s Chart of Biography which repre-sented individual timelines over a six hundred year span.
Figure 3. The Simpsons family structure represented with GeneaQuilts.
resembles a timeline where the nodes converge and diverge toform groups and community structures [12]. Zhao’s EgoLinesis another approach where a subway metaphor is used to rep-resent ego-centric collaboration networks through time [14].Specifically concerning genealogical trees, GeneaQuilts [2]presents a system for the exploration of large genealogy treesthrough a synthetic matrix representation (see Figure 3).
While these tools have addressed the issues of scalability andtime-bound genealogical data representation, we have foundthat for art historians, the type of data and variety of tasks athand require more adapted visualization tools. In this context,constraints such as sparse datasets, small sizes of populationsof interest, and the strong dependency on the time dimensionof each data point have created the need for a new approach torepresenting genealogical networks.
With NAHR, we contribute a new approach for the representa-tion of complex genealogical and extra-familial relationshipsthat accommodates different types of links, while centeringtheir time-boundedness. This tool combines the familiarity offamily tree visualizations to represent personal links with anincorporated way to explore the time dimension and its impacton the dynamics of a community.
DATA, USERS, AND TASKS
MethodologyThe initial phase consisted of gathering user needs by conduct-ing interviews with five expert users, including two art historyprofessors and three PhD students. We also designed a surveythat was distributed and filled by 11 students and professorsof art history. These steps allowed us to derive user profiles,task descriptions and a better understanding of the data usage.
The following steps consisted of iterations between design oflow-resolution paper prototypes and feedback from users. Thelast step was the delivery of an implemented proof of conceptof the final prototype. The next sections describe the nature ofthe data represented, characteristics on the users, as well as alist of the extracted tasks.
DataThe data consists of genealogical trees with additional god-parenthood and professional relationships among persons.These extended family trees is what we refer to as communities.They are built around persons (also referred to as actors), thatare each described with a name, dates of birth and death, gen-der and - in some cases - profession. Each actor is connectedto other actors through 3 possible types of links.
• Marriage and descendance: each actor has a link to theirparents, as well as potential spouse(s) and offspring.
• God-parenthood links: god-parenthood is an importantmarker of social links in the described social context. Itallowed families to build connections to one another, orstrengthen existing ones. Moreover, godparents can endup becoming masters for their godchildren or influencingtheir style, they are therefore critical to identify. The datareflects this, as each actor has a link to their godmother andgodfather, if known.
• Professional links: few actors have known professions.Some of them, however, are known to have collaborated.Collaborations can be of different types, but our currentdataset only contains joint ventures.
The data also includes time-bound aspects. Births and deathdates, marriages, as well as beginnings and ends of profes-sional collaborations are denoted as events.
The database that was assembled for this project consists ofthousands of actors. For this project, however, we were given asmall subset consisting of 32 actors spread over 3 generations.This dataset was selected by our users because it was thedensity and size of a typical community size that they wouldbe interested in visualizing (see User Characterisation). Thefocus was therefore on building an interesting approach torepresenting a dataset of this nature.
User characterisationTwo broad categories of user backgrounds are targeted by thistool. The first and main one is the art history research commu-nity. This interface is made as a proof of concept to introducean effective visualization to explore community dynamics. Itis meant to present a usable tool allowing to explore the dataand learn new aspects of the dynamics between actors, whileat the same time acting as a sample project show-casing theadded value of integrating visualization to the data analysisprocess in art history. The second category is a broader non-expert audience: the tool is also designed to attract fundingfor the project, and interest for the digital humanities fieldin general. In that aspect, the interface should be viewed asusable and fun by non-domain users.
A look at the literature on art history research, interviews anda survey ran on 11 art history researchers allowed us to makethe following data usage-related observations:
• For most of the participants, the number of subjects studiedat once was quite low. This was also addressed by users ininterviews, as large dataset representations or full databasevisualizations were expressly discouraged. One user notedthat "Many people from computer science make these toolsfor us where they want to show all the data that we have inone big graph, but that’s simply not how we work". It seemsindeed that researchers in the field of art history are morelikely to focus on finding out more about a single artist, ora selected few, and a specific era. For 8 out of 11 personssurveyed, we found that a tool allowing to visualize groupsof less than 50 persons at once was sufficient.
• The interest - and acceptance - for digital tools are still gen-erally low. The literature confirmed what we found in ourown research, which is that the interest in visualization toolsseems to be a relatively new trend in the field of art history.In 2005, the Summit on Digital Tools at the University ofVirginia found that only about six percent of humanist schol-ars "go beyond general purpose information technology anduse digital resources and more complex digital tools intheir scholarship." General purpose information technologyhere referring to tools for word processing, presentations,spreadsheets and conferencing [3]. Later, in 2012, Gibbs &Owens find that digital tools are still "a fringe element" inthe humanities field. Although the interest seemed higher,a lack of usability in the existing tools was pointed out [4].According to our survey and interviews, while most partic-ipants reported being already familiar with charting tools,the most cited one was the spreadsheet Excel. Its ease ofuse for chart creation was cited as a positive point, althoughit is clear it is not enough to produce adapted interactivevisuals. This tells us that familiarity and acceptance mightbe critical aspects to cater for in any new tool we would liketo introduce.
• According to our users, most of the current art history re-search workflow is carried offline. As an example to illus-trate this, our main users work on physical archives fromthe national libraries of the cities of Brussels and Antwerpin Belgium. The process of retrieving data, therefore, in-volves tracking down records, transcribing information, andarchiving said data in a local format for future referencing.For this reason, most participants expressed interest in in-corporating a digital visualization tool to their data analysisworkflow. While this seems to contradict the previous pointat first sight, it appears to be a consensus that the field ofdigital humanities is "as buoyed by optimism as it is ladenwith skepticism" [7].
A tool that can bring added features to familiar representationsof the data can therefore be of great value to the community.We believe that a tool that is designed in a user-centered ap-proach and that centers the tasks performed by researchers hasthe potential to overcome the skepticism caused by a lack ofusability and the use of unadapted generic tools.
Figure 4. Main visualization before interaction.
TasksThe conducted interviews and survey allowed us to extractthree categories of tasks with which NAHR can assist. First,actor-related tasks are the ones centered around gaining knowl-edge on actors and relationships. Chronology-related tasksinvolve being able to navigate in the time dimension. Finally,in the research-focused tasks, we list the tasks that are relatedto the users’ work-flow rather than the data. Overall, we es-tablished that users focused on analyzing specific subsets ofpersons and relationships, rather than the whole dataset. Thisallowed us to focus on detail views and interactions instead ofdesigning larger overview visualizations.
Actor-related tasks• (T1) Viewing an actor in the context of their community
relationships: Users are interested in seeing one or moreactors as they interact with their community in terms ofmarriage and descendance, as well as god-parenthood, andprofessional collaborations. The system should show allthese relationships and make it easy for the user to distin-guish between their nature, as well as choose which type ofrelationships to view or dismiss.
• (T2) Understanding passed actors legacy: Family linksare critical community structures and therefore should beclearly exposed. The family bonds should be visible evenafter the members’ deaths as their place in the communityremains existing and their impact may stay valid.
• (T3) Estimating the relationship density of an actor: Anactor’s influence on the community may be estimated fromthe number of links they have to other members. As thischanges through time, a user can have an idea of the changein dynamics around a user by looking at how much the
density of their ties varies. The system should allow a userto view that information over time, as well as in a specificpoint in time.
Chronology-related tasks• (T4) Freezing the story in time: Users are sometimes inter-
ested in looking at the ties and dynamics between membersof a community frozen in a specific year. The system shouldallow them to explore the ties in a chosen point in time.
• (T5) Focusing on the time periods with most activity: Someperiods in time witness more events than others. The systemshould assist the users in knowing the years with mostchanges in dynamics.
Research-focused tasks• (T6) Having access to references of evidence of presented
data: Users who want to pursue their research on a partic-ular actor, or investigate the validity of the data displayedin the visualization, should be able to access a reference tothe origin of this information. The system should presentusers with evidence of the data and direct them to its initialsources.
• (T7) Saving and sharing an image of a community in apoint in time: Users work in collaboration to analyze theirdatasets. They want to be able to share a specific configura-tion of community links at a point in time to colleagues inorder to share an insight or discovery. The system shouldallow them to save "images" of the current configurationthey are looking at to save for future retrieval or to forwardto one another.
Figure 5. Main visualization after year selection and links enabling.
DESIGNThe main interface consists of a graph visualization describinga family structure where the nodes are actors in the dataset,and links are ancestry relationships. Figure 4 shows the vi-sualization before any interaction, when only the communitystructure is drawn. Figure 5 shows the tool after a desired yearwas selected, and the links were enabled, showing only whatthe community links were like in said year.
In this part, we will define how families are represented, howthe time aspect is sewn into the tree view, and what is encodedin the nodes and links of the graph. Each task being supportedis referred to at the description of the feature supporting it withthe notation (Ti), i being the task number defined in Tasks.
The final subsection contains a reading of the graph in Figures4 and 5 to illustrate how different features work together totell the story of the dataset.
Structure
Graph StructureThe genealogical aspect of the data being central to our users’research, we chose to address T1 by representing the datain a common family tree mapping to leverage the familiaritythis representation has. This decision was validated after aninitial family tree representation of the dataset was found to beeasily readable, albeit not sufficient to capture all informationincluded in the data.
Parents of the first generation available in the dataset are drawnone below the other in separate regions denoting separatefamilies. Each generation is aligned on the y dimension, andsiblings are vertically ordered in birth date chronological order.
The decision to use a generational view of the family linksinstead of a representation aligned on an absolute view oftime was a preference indicated by users, as generations areimportant tools to analyze the data according to our interviews.
Finally, the family tree was drawn horizontally instead of ver-tically to allow for a readable node width across generations,we found that this did not impede readability.
TimeWe chose to use the x and y dimensions for the network as-pect of the data only in order to ease graph comprehension.Therefore, an extra dimension was needed to include the timeaspect. For that purpose, a vertical slider was added, allowingthe user the following two tasks:
• Selecting a current year: dragging the handle to a specificyear updates the nodes and links to represent the currentstate of persons and relationships in that year (T4).
• Providing an overview of event frequency through the years:The year selection slider also acts as a bar chart where eachsquare represents an event happening in the correspondingyear. The squares are color-coded in the same way as thelinks (Figure 8) to ensure differentiability between differ-ent types of events, namely births and deaths, professionalcooperations and god-parenthood links (T5).
NodesA person is represented by an annotated bar - or life bar -containing the information pertaining to them. Each bar is arectangle of fixed height, where the width is proportional tothe lifespan of the person. For each selected year in time, anactor’s life bar will proportionally fill to indicate their currentage. It will be grayed out after death to reduce its saliency (see
Figure 6. Node representation before an actor’s birth, during their lifeand after their death.
Figure 7. Signification of profession-related icons used in the visualiza-tion.2
Figure 6). This allows the family to retain all its members,alive, future, or deceased, while giving saliency to the currentfamily structure only (T2).
The life bar is annotated with the name of the actor, a sym-bol representing their gender, and an icon representing theirprofession, if known. Figure 7 contains a legend of the usedsymbols.
More information about a person is also visible upon hovering(see Figure 9 (a)). Once the reference documents used to buildthe database are fully digitized, this tool-tip will also containlinks to view to said documents (T6).
LinksAs described in the Data section, three types of relationshipsare defined in our dataset. Different link encodings representthese relationships as can be seen in Figure 8.
The family link is drawn in a typical "family tree" manner.God-parenthood and professional relationships can be toggledon or off the screen to allow for more readability, and tosupport different tasks. These relationships are representedwith colored dashed lines to differentiate their appearancefrom the family ones. They are drawn using thick lines tobalance the lighter color and maintain visibility.
We retain all links visible after actors pass away and collabo-rations end to support T2 and T3. The saturation encoding istherefore used to express the validity of a link at selected year.
2The icons used to represent professions are made available by theirauthors on flaticon.com. The merchant and tapestry producer iconswere designed by Freepik, the painter icon was designed by GoodWare
As with the nodes, hovering will display a tool-tip containingadditional information regarding the relationship (see Figure9 (b)).
Figure 8. Representation of link states before, during and after exis-tence, according to the type of relationship.
Figure 9. Hovering on a node (a) or a link (b) will display a tooltipcontaining additional information about the element.
Reading the graph: A walk-throughAfter having described the features of the tool, we suggesta walk-through of the graph in Figures 4 and 5 to present asynthetic illustration of how the elements function together inthis use case.
Users are first presented with the view in Figure 4. It is asideways family tree representing 3 families across three gen-erations. Each of the three couples in the leftmost columnare parents of a different family whose offspring are linked tothem through curved black lines. A third generation is alsoshown and linked to its parents the same way. On the right, wecan see a vertical slider where the handle is in initial positionbefore any event is recorded. The values along its length arethe years for which we have data on the persons displayed. We
can see that we have historical records on the studied personsbetween the years of 1600 and 1720. The year slider is alsoannotated with a vertical modified bar chart representing thenumber of events recorded for each year. We can see for in-stance that the first birth (in black) was recorded in 1600, andthe last death (in gray) in 1720.
If they would like to see the ongoing community dynamics ata specific point in time, the user only needs to slide the handleto the chosen year, this will update the filling of the barsrepresenting each person in the graph to reflect their currentage. Figure 5 shows the shape of this community in the year1640. We can see that the first generation is around the middleof their lives and have already had their first children. Two ofwhich (Gielis Van Leefdael and Johannes Van der Strecken)have been grayed out, meaning they have already passed awayafter a short time alive. The rest of the persons have not beenborn yet at this time. Hovering on any of these persons willindicate more specific information about birth year, death year,and profession.
If we toggle the professional and god-parenthood links, wecan also see the extra-familial dynamics happening at thesame time. We can first see that there were several jointventures happening between Jan Van Leefdael and Maria VanLeefdael’s husband: Gerard Van der Strecken, one of whichwas taking place during 1640. We can also have insight as tothe two families turning to one another for god-parenthoodroles for their children. In the background, we can see futurelinks that will gain in visibility when the equivalent time pointis selected.
RECEPTIONThe tool was first introduced to our five expert users for feed-back, where it was used for a couple of weeks before com-ments were heard. It was later presented in art history sym-posia 3 and workshops to audiences of art historians specif-ically, as well as broader publics of humanities researchers,where it was received as an innovative and easy-to-use solu-tion. More feedback received about specifics of the tool isdescribed below.
• Structure representation: Most of the users were presentedthe tool after having first seen the same data in a traditionalfamily tree representation, then using a force directed layout.They reported the genealogy structure in our tool was easyto understand, as it uses the codes of a traditional familytree. The users appreciated the ability to scroll through timeand view concurrent events and links through interactionwith the tool, as well as the overview provided by the eventfrequency view embedded in the slider.
• Nodes and links representation:The representation of links was well received. Family struc-ture and professional links gave a clear idea of the com-munity connections. God-parenthood links were thought
3 Venues the tool was presented in include the Baroque Tapestryand the Rome of the Barberini Symposium (USA, 2017), the An-nual Meeting of the Renaissance Society of America Symposium(USA, 2018), and the Equip & Engage: Research and DisseminationInfrastructures for the Humanities Symposium (Belgium, 2018).
to still have the potential to be confusing as they are moreprevalent in the dataset, therefore are more likely to resultin clutter.
The display of the dynamics around three generations inone frame was seen as a positive aspect, although the needwas raised to allow following these family trees further inthe past or the future.
CONCLUSION AND FUTURE WORKSIn this project, we developed a visualization tool aimed atrepresenting small networks of extended families and commu-nities for art history researchers. We found that researchers inart history needed small scaled, highly adapted systems ratherthan large scale data exhaustive visualizations. In the pro-cess of developing the NAHR interface, we found that classicfamily tree representations were in themselves not sufficientto support the users’ tasks, but that a redesigned interactivefamily-tree based visualization that added time as a third di-mension was successful in supporting user tasks and earninginitial acceptance.
This tool is to be integrated into an online database containingall the actors found in our users archives, where users will beable to search for specific persons and visualize their familytrees. Next steps will therefore be to develop ego-centeredviews built around each actor to allow users to explore theirnetworks. We are hoping this will enable our users to have aseamless experience navigating back and forth between writtendescriptions of actors and the visual representation of the samedata on NAHR. We also want to investigate collaborationaspects and implement features that can address T7.
Because our users are only interested in studying a limitednumber of actors at once, the scalability factor was not ad-dressed in the design we presented. However, were that use-case to evolve, visual and interaction scalability will have tobe addressed.
Future work also includes formal user testing to compareNAHR with other existing methods and improve its design.
ACKNOWLEDGEMENTSThis research is part of Project Cornelia, a project funded bythe University of Leuven (KU Leuven) and the Flemish Fundfor Scientific Research-Belgium (FWO-Vlaanderen).
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